LOGISTICS

Learn How a Fortune 500 Transportation & Logistics Giant Reduced Their Data Costs by $500,000/year

Background

Transportation and logistics is a brutally competitive and equally complex industry wherein even the biggest players are continuously finding ways to bring down overheads and achieve process acceleration and seamlessness.

We were able to help a global transportation company with a rich history of freight and transit excellence solve a host of limitations and challenges resulting from a legacy approach to data collection and use. This prevented them from collecting relevant information in real-time to make data-driven, ROI-based decisions. Despite spending millions of dollars on getting a 360° view of their business, critical business intelligence evaded them. This interfered with their attempts to streamline logistics and freight management, bring down the cost, and enhance processes.

Problems

We had been collaborating with this client for some time on their machine learning use-case for predicting vessel ETA (Estimated Time of Arrival). Through the course of this engagement, we unearthed numerous data-centric challenges and realized the need for a focused overhaul of their data strategy. Their problems needed a solution that laid stress on outcomes and maximized the potential of their Data to Outcomes journey. A week long Discovery Workshop with their shipping team executives helped us identify the following pain-points:

  • Data was being manually aggregated periodically, by monitoring 11 port websites, resulting in inefficient workflows due to an inability to access data in real-time. This led to an increase in the scheduling of empty returns because operational teams had no clear visibility into data from terminals, shipping lines, and other sources. Dynamic planning of empty returns eluded them
  • Lack of insights into vessel ETA, as they were facing difficulties in accessing terminal, container, and maritime data and cross-pollinating it with internal data. This created chaos in cargo and reefer handling at the terminal due to data delay and inefficacy in crunching relevant data
  • Predictive analytics was a challenge as they had no historical data to analyze and couldn’t identify patterns that could help build the right predictive models. This impeded their ability to optimize the process of scheduling pickups at the gate. Appointment booking became a tedious and problem-ridden exercise as the logistics teams couldn’t get the right information about gate hours, vessel schedule, and container sources. Data orchestration was messy, resulting in scheduling inaccuracies and shooting up overheads
  • Poor data collection methodology resulted in bad quality data collection and underutilization of internal data; failure to capture data from relevant external sources made it virtually impossible to meet the highest standards of quality

Solutions

DX Factor’s in-house team of data experts leveraged DXFactor’s AI and data-driven recommendation engine to build and deploy a dynamic next-gen intelligence platform that synchronizes across all TMS (Transportation Management System) activity, and external reporting tools. The objective behind this solution is to make sure relevant data is available to all stakeholders in real-time.

We understand that data is in a constant state of flux and therefore any change in data triggers customer procedural rules to ensure compliance. The solution also delivers robust automated notifications and alerts that offer comprehensive information and updates on terminals, shipping lines, gate hours, empty returns, vessel schedules, and miscellaneous notes at all levels.

Data Scrapping Platform

  • Easy to Use Interface and 360 Degree View
    A workshop with all stakeholders allowed us to understand core problems and user personas. This process resulted in a fluid portal that is extremely intuitive and helps users manage and track transportation and logistics operations effortlessly.We adopted a ‘single pane of glass’ approach to ensure a holistic, consolidated, and standardized view of all information collected from disparate sources. Users now have a ringside view of Empty Returns, Vessel Schedule, Gate Hours, and Container Status Information.
  • Empowered Notifications
    We incorporated notifications when we realized their criticality in day-to-day workflows and the important role they play in driving successful logistics outcomes. Users can subscribe to email and text notifications, which keeps them on top of things. Any change in Empty Returns, Gate Hours, and Vessel Scheduling, and stakeholders are immediately notified.
  • Smarter and Profitable Appointments
    Outdated scheduling workflows were done away with and advanced data analytics systems were put in place to analyze history, shipment information, and terminal status for seamless, automated and smart appointment scheduling.
  • Built Intelligent Workflows and Integrated Automation
    Five pillars namely Business Rules, User Experience, Deprecation of Ineffective Processes, Scalable Technology, and an Outcomes Focus were used to create the entire workflow and automation component. Users can now configure data-backed workflows that trigger a series of automation with zero human intervention. This has improved productivity, process efficiency, and given our client a huge competitive edge.
  • Outcomes Driven Predictive Analytics
    In-depth historical data analysis showcased how this data can be used to build predictive models backed by AI for better decision making and improved forecasting. While the client did not think this was one of their top priorities, they got sold on this idea when we offered a POC that delivered razor-sharp business insights. Our analytics now creates prediction models for vessel ETA, on-port container arrangement, empty return recommendation, and resource forecasting to help teams derive optimum value from their day-to-day operations.
  • Leveraging Meaningful External Data
    Proprietary external data is extensively used in prescriptive and predictive analytics to improve insights offered by predictive modeling. External data libraries exploited include satellite data, ocean data, weather data, and more.

Outcomes

DXFactor revolutionized the client’s transportation and logistics data strategy by making it more cutting edge and results-oriented. This solution has proven to be so successful that we are now tasked with extending this solution to the rail and road arms of the client. It has achieved:

  • $500,000 reduction in their data aggregation costs per year.
  • 20 other teams and 85 stakeholders benefit from the Data Scraping Platform; they use its dashboard to gain actionable insights and subscribe to regular notifications.
  • 82% vessel ETA prediction accuracy with a predictive model built using captured historical data and our proprietary data.

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